For an autonomous vehicle, intelligently identifying objects ahead of it, inferring the information, and providing accurate inputs to a control system of the autonomous vehicle is very important for smooth navigation. The information regarding identified objects helps the control system of autonomous vehicle to guide the vehicle in correct manner in order to avoid any collision and breakdown. In other words, this information may help the control system to imitate human intelligence.
However, while a vehicle is moving, capturing images of the scene in front of the vehicle and identifying objects in the image at the same speed as human perception is tedious in case of autonomous vehicle. The reason being that a lot of data collection and processing is involved in limited computing systems configuration. Hence it is difficult to catchup at higher speeds of the vehicle.
In conventional solutions, the control systems are trained only once with the available limited set of data (or manual training data). However, this is not sufficient as there will be new and different objects observed every other day during real time vehicle navigation. This conventional solution thus frequently requires manual trainings. However, one-time training with large data set is difficult, as relevant data has to be collected, identified, and then classified, before it can be used to train the control systems.